Abstract The growing reliance on Electronic Document Management Systems (EDMSs) in public institutions necessitates an improved understanding of usability across diverse user groups. This study evaluates the perceived usability of a widely deployed commercial EDMS used in a public university context by examining differences across gender, age, personnel type (academic/administrative), education level, and prior EDMS experience. A mixed-methods design integrates objective task-based performance measures (completion, time, and perceived difficulty) and the System Usability Scale (SUS-TR) with qualitative feedback collected via open-ended questions. Overall, users valued the system’s contribution to streamlining document workflows; however, they reported notable usability barriers, particularly related to complexity and navigation. No significant differences were observed by gender, age category, or prior EDMS experience, whereas administrative staff and participants with higher education levels reported higher SUS scores. The mean SUS score (53.25) indicates below-average perceived usability when interpreted against established benchmarks, suggesting the need for targeted usability improvements. Qualitative feedback further highlights the need to simplify interaction flows – particularly around document search, dispatch, and leave management – and to enhance training and support resources to address recurring usability issues and reduce user errors. The study offers actionable recommendations for improving EDMS usability in public institutions and underscores the importance of user-centred design in digital transformation initiatives.

Abstract The growing problem of air pollution by fine particulate matter (PM2.5) from local sources, such as boiler houses or small industrial facilities, requires effective and accessible assessment tools. A significant gap exists between complex, resource-intensive dispersion models used in research and the practical needs of engineers, ecologists, and regulatory bodies who require instruments for rapid operational analysis. This problem is particularly acute in regions like Ukraine, where access to real-time, high-resolution environmental data is limited, and regulatory practices often rely on legacy methodologies. The paper describes the development and testing of a desktop software application with a graphical user interface (GUI) designed for scenario modelling (“what if” analysis) and quantitative assessment of air pollution levels from a local source. The core of the software tool is based on an adapted Gaussian plume analytical model, which calculates pollutant dispersion considering meteorological conditions and source parameters. The system integrates a developed method for integral impact assessment, categorising the pollution level based on calculated concentrations. The developed software allows the user to interactively input the constructive (stack height, diameter) and operational (emission rate) parameters of a pollution source, as well as current meteorological conditions. The system provides an instantaneous calculation of the expected PM2.5 concentration at a given point and classifies the impact: “Low”, “Moderate”, “High”, or “Very High”. The developed tool brings practical value, supporting the decision-making process. It provides a means for the operational monitoring of environmental impact and the preliminary planning of measures to reduce the ecological load from local pollution sources, making complex analysis accessible to a wider range of specialists, especially in data scarce environments.

Abstract Free-form complex surfaces are prevalent in modern graphic applications. With the increasing prevalence of complex 3D surfaces enabled by advances in range scanning and 3D printing technologies, minimising parameterization times for large meshes has become crucial. This paper proposes an efficient approach for the planar development of convex free-form mesh patches using an improved energy-based technique with a variable step-size algorithm. Building upon the energy model of Wang et al., our study addresses the limitations of conventional energy dissipation algorithms, which employ fixed step sizes. The proposed variable step-size method, particularly suitable for convex or disk-shaped mesh surfaces, dynamically adjusts steps, significantly reducing energy dissipation iterations. Leveraging our previous geometric flattening method, we further enhance planar surface development using an advanced mass-spring-based approach. Here, we show that our method accelerates the mechanical flattening process while maintaining high accuracy, achieving a shape error of 0.400 and an area error of 0.147 after 36 iterations for the Surf1 patch, reducing the required iterations by nearly half compared to the fixed step-size method. This study contributes to advancing the field of surface parameterization and flattening, with potential applications in various industries.

Abstract An approach to speed up the DBSCAN algorithm is suggested. The planar clusters to be revealed are assumed to be tightly packed and correlated constituting, thus, a serpentine dataset developing rightwards or leftwards as time goes on. The dataset is initially divided into a few sub-datasets along the time axis, whereupon the best neighbourhood radius is determined over the first sub-dataset and the standard DBSCAN algorithm is run over all the sub-datasets by the best neighbourhood radius. To find the best neighbourhood radius, it is necessary to know ground truth cluster labels of points within a region. The factual speedup registered in a series of 80 000 dataset computational simulations ranges from 5.0365 to 724.7633 having a trend to increase as the dataset size increases.

Abstract We study the feasibility of video transmission over optical fibre to optimise bandwidth with the implementation of HEVC codec features. We use simulation (Matlab and the OptiSystem software). Different values of the CRF are used to evaluate its impact on the visual quality and the size of the encoded file, as well as its influence on the video transmission performance. The simulation results show that by adjusting the CRF, the encoders can optimise the compression of the video data to reduce the file size while preserving an acceptable visual quality. This makes it possible to adapt the transmission to the bandwidth constraints of the optical fibre, by choosing higher CRF values to reduce the size of the files and save bandwidth, or lower values to maintain optimal quality when the bandwidth is sufficient. In addition, from the optical fibre point of view, the dispersion weakens and the eye opens, and it is observed that the length of the fibres is inversely proportional to the signal transmission quality. Thus, the judicious use of different CRF values can contribute to efficient and high-quality video transmission via optical fibre.

Abstract The Coronavirus is a virus that spreads very quickly. Therefore, it has had very destructive effects in many areas worldwide. Because X-ray images are an easily accessible, fast, and inexpensive method, they are widely used worldwide to diagnose COVID-19. This study tried detecting COVID-19 from X-ray images using pre-trained VGG16, VGG19, InceptionV3, and Resnet50 CNN architectures and modified versions of these architectures. The fully connected layers of the pre-trained architectures have been reorganized in the modified CNN architectures. These architectures were trained on binary and three-class datasets, revealing their classification performance. The data set was collected from four different sources and consisted of 594 COVID-19, 1345 viral pneumonia, and 1341 normal X-ray images. Models are built using Tensorflow and Keras Libraries with Python programming language. Preprocessing was performed on the dataset by applying resizing, normalization, and one hot encoding operation. Model performances were evaluated according to many performance metrics such as recall, specificity, accuracy, precision, F1-score, confusion matrix, ROC analysis, etc., using 5-fold cross-validation. The highest classification performance was obtained in the modified VGG19 model with 99.84 % accuracy for binary classification (COVID-19 vs. Normal) and in the modified VGG16 model with 98.26 % accuracy for triple classification (COVID-19 vs. Pneumonia vs. Normal). These models have a higher accuracy rate than other studies in the literature. In addition, the number of COVID-19 X-ray images in the dataset used in this study is approximately two times higher than in other studies. Since it is obtained from different sources, it is irregular and does not have a standard. Despite this, it is noteworthy that higher classification performance was achieved than in previous studies. Modified VGG16 and VGG19 models (available at github.com/akaraci/LargeDatasetCovid19) can be used as an auxiliary tool in slight healthcare organizations’ shortage of specialists to detect COVID-19.

Abstract Multimodal biometrics is the technique of using multiple modalities on a single system. This allows us to overcome the limitations of unimodal systems, such as the inability to acquire data from certain individuals or intentional fraud, while improving recognition performance. In this paper, a study of score normalization and its impact on the performance of the system is performed. The fusion of scores requires prior normalisation before applying a weighted sum fusion that separates impostor and genuine scores into a common interval with close ranges. The experiments were carried out on three biometric databases. The results show that the proposed strategy performs very encouragingly, especially in combination with Empirical Modal Decomposition (EMD). The proposed fusion system shows good performance. The best result is obtained by merging the globality online signature and fingerprint where an EER of 1.69 % is obtained by normalizing the scores according to the Min-Max method.

Abstract The research examines the accuracy of current solution models for the Arabic text sentiment classification, including traditional machine learning and deep learning algorithms. The main aim is to detect the opinion and emotion expressed in Telecom companies’ customers tweets. Three supervised machine learning algorithms, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF), and one deep learning algorithm, Convolutional Neural Network (CNN) were applied to classify the sentiment of 1098 unique Arabic textual tweets. The research results show that deep learning CNN using Word Embedding achieved higher performance in terms of accuracy with F1 score = 0.81. Furthermore, in the aspect classification task, the results reveal that applying Part of Speech (POS) features with deep learning CNN algorithm was efficient and reached 75 % accuracy using a dataset consisting of 1277 tweets. Additionally, in this study, we added an additional task of extracting the geographical location information from the tweet content. The location detection model achieved the following precision values: 0.6 and 0.89 for both Point of Interest (POI) and city (CIT).

Abstract Due to the rapid growth of the Internet of Things (IoT), researchers have demonstrated various IoT solutions, which are used to interconnect a wide range of IoT devices through the Internet. However, IoT stumbled into vertical silos; the available solutions provide specific IoT infrastructure, devices, protocols, data formats and models. This diversity and heterogeneity lead to interoperability issues. Heterogeneity happens at all IoT layers, especially at the application layer; devices often adopt mutually incompatible application-layer communication protocols to connect devices to IoT services. Furthermore, in order to integrate semantics to raw data, each system uses its one domain-specific ontology to make data more understandable and interpretable by adding semantic annotations. Working in isolation reduces the interoperability among IoT devices and systems, things across domains need to internetwork and collaborate to provide high level IoT services. Therefore, to alleviate the problem of both communication protocol interoperability and semantic interoperability across vertical silos of systems at the application layer, this paper proposes a semantic gateway (SGIoT) that acts as a bridge between heterogeneous sink nodes at the physical level and IoT services. SGIoT enables interconnectivity between communication protocols such as CoAP and MQTT regardless of their communication model, meanwhile it enables semantics integration throu gh cross-domain ontology (CDOnto) for semantic annotation, in order to provide interpretation of messages among IoT applications across domains. Our approach focuses on modularity and extensibility.

Abstract The Content Based Image Retrieval (CBIR) system is a framework for finding images from huge datasets that are similar to a given image. The main component of CBIR system is the strategy for retrieval of images. There are many strategies available and most of these rely on single feature extraction. The single feature-based strategy may not be efficient for all types of images. Similarly, due to a larger set of data, image retrieval may become inefficient. Hence, this article proposes a system that comprises of two-stage retrieval with different features at every stage where the first stage will be coarse retrieval and the second will be fine retrieval. The proposed framework is validated on standard benchmark images and compared with existing frameworks. The results are recorded in graphical and numerical form, thus supporting the efficiency of the proposed system.